52 research outputs found
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics
A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms
This paper proposes a computational approach for generation of reference path
for upper-limb exoskeletons considering the scapulohumeral rhythms of the
shoulder. The proposed method can be used in upper-limb exoskeletons with 3
Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of
supporting shoulder girdle. The developed computational method is based on
Central Nervous System (CNS) governing rules. Existing computational reference
generation methods are based on the assumption of fixed shoulder center during
motions. This assumption can be considered valid for reaching movements with
limited range of motion (RoM). However, most upper limb motions such as
Activities of Daily Living (ADL) include large scale inward and outward
reaching motions, during which the center of shoulder joint moves
significantly. The proposed method generates the reference motion based on a
simple model of human arm and a transformation can be used to map the developed
motion for other exoskeleton with different kinematics. Comparison of the model
outputs with experimental results of healthy subjects performing ADL, show that
the proposed model is able to reproduce human-like motions.Comment: In 2017 IEEE International Symposium on Wearable & Rehabilitation
Robotics (WeRob2017
Real-Time Semantic Segmentation using Hyperspectral Images for Mapping Unstructured and Unknown Environments
Autonomous navigation in unstructured off-road environments is greatly
improved by semantic scene understanding. Conventional image processing
algorithms are difficult to implement and lack robustness due to a lack of
structure and high variability across off-road environments. The use of neural
networks and machine learning can overcome the previous challenges but they
require large labeled data sets for training. In our work we propose the use of
hyperspectral images for real-time pixel-wise semantic classification and
segmentation, without the need of any prior training data. The resulting
segmented image is processed to extract, filter, and approximate objects as
polygons, using a polygon approximation algorithm. The resulting polygons are
then used to generate a semantic map of the environment. Using our framework.
we show the capability to add new semantic classes in run-time for
classification. The proposed methodology is also shown to operate in real-time
and produce outputs at a frequency of 1Hz, using high resolution hyperspectral
images
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